Considering that most existing 3D human datasets lack dense visibility annotations, we obtain pseudo ground-truths from dense UV estimations [1]}.
Given the estimated UV map of an image, we calculate the pixel-to-vertex correspondence by minimizing the distance of their UV coordinates.
Each vertex mapped to a human pixel is considered visible, and vice versa.
Note that this covers the cases of truncation, self-occlusions, and occlusions by other objects.
We further show that the dense vertex-to-pixel correspondence provides a good supervisory signal to localize vertices in the image space.
Since dense UV estimations are based on part-wise segmentation masks which are robust to partial-body images, the dense correspondence loss can mitigate the inaccurate pseudo ground-truth meshes and better align the outputs with human silhouettes.
To demonstrate the effectiveness of our method, we conduct extensive experiments on multiple human datasets used by prior arts.
Both qualitative and quantitative results on the Human3.6M [2]}, 3DPW [3]}, 3DPW-OCC [3]}, [5]}, and 3DOH [5]} datasets show that learning visibility significantly improves the accuracy of dense human body estimation, especially on images with truncated or occluded human bodies.
